Image super-resolution is directed to estimating a high-resolution (high-resolution) image from low-resolution (low-resolution) input. There are mainly three categories of approach for this problem, namely interpolation-based methods, reconstruction-based methods, and learning-based methods.
Interpolation-based methods are simple but tend to blur the high frequency details. Reconstruction-based methods enforce a reconstruction constraint, which requires that the smoothed and down-sampled version of the high-resolution image need to be close to the low-resolution image. Learning-based methods “hallucinate” high frequency details from a training set of high-resolution/low-resolution image pairs. The learning-based approach relies to a significant extent on the similarity between the training set and the test set. It is not clear how many training examples are sufficient for generic images.
To design a good image super-resolution algorithm, a significant issue is how to apply a good prior or constraint on the high-resolution image. Any improvement in image super-resolution technology is beneficial.